Introduction to summarytabl
summarytabl-intro.RmdWelcome to the summarytabl package! This package makes
it easy to create simple tables for summarizing continuous, ordinal, and
categorical data. This document introduces you to some of
summarytabl’s most frequently used functions, along with
examples of how to apply them to your data frames.
To begin, load summarytabl
Types of functions
This package has three types of functions to help you summarize your data. Those for:
- Categorical data, such as binary (e.g., Unselected/Selected) or nominal (e.g., woman/man/non-binary) variables
- Multiple response data, including binary (e.g., Unselected/Selected), ordinal (e.g., never, sometimes, often), or Likert and Likert-type (e.g., strongly disagree to strongly agree) variables
- Continuous data, like interval (e.g., test scores) and ratio-level (e.g., age) variables
Functions for categorical data start with cat_, those
for multiple response data start with select_, and
functions for continuous data start with mean_.
To learn more about how these functions work, read the next few sections.
Categorical variables
Summarize a categorical variable
The cat_tbl() function can be used to generate a
frequency table for a categorical variable.
cat_tbl(data = nlsy, var = "race")## # A tibble: 3 × 3
## race count percent
## <chr> <int> <dbl>
## 1 Black 868 0.292
## 2 Hispanic 631 0.212
## 3 Non-Black,Non-Hispanic 1477 0.496You can exclude certain values and eliminate missing values from the
data using the ignore and na.rm arguments,
respectively.
cat_tbl(data = nlsy,
var = "race",
ignore = "Black",
na.rm = TRUE)## # A tibble: 2 × 3
## race count percent
## <chr> <int> <dbl>
## 1 Hispanic 631 0.299
## 2 Non-Black,Non-Hispanic 1477 0.701Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
cat_tbl(data = nlsy,
var = "race",
ignore = "Black",
na.rm = TRUE)## # A tibble: 2 × 3
## race count percent
## <chr> <int> <dbl>
## 1 Hispanic 631 0.299
## 2 Non-Black,Non-Hispanic 1477 0.701
# Counts only
cat_tbl(data = nlsy,
var = "race",
ignore = "Black",
na.rm = TRUE,
only = "count")## # A tibble: 2 × 2
## race count
## <chr> <int>
## 1 Hispanic 631
## 2 Non-Black,Non-Hispanic 1477
# Percents only
cat_tbl(data = nlsy,
var = "race",
ignore = "Black",
na.rm = TRUE,
only = "percent")## # A tibble: 2 × 2
## race percent
## <chr> <dbl>
## 1 Hispanic 0.299
## 2 Non-Black,Non-Hispanic 0.701Comparing two categorical variables
To create a grouped frequency table for two categorical variables
(i.e., a contingency table), use the cat_group_tbl()
function.
cat_group_tbl(data = nlsy,
row_var = "gender",
col_var = "bthwht")## # A tibble: 4 × 4
## gender bthwht count percent
## <dbl> <dbl> <int> <dbl>
## 1 0 0 1340 0.450
## 2 0 1 123 0.0413
## 3 1 0 1409 0.473
## 4 1 1 104 0.0349Like cat_tbl(), you have the option to exclude certain
values and omit missing values (by row and/or column).
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
ignore = c(race = "Non-Black,Non-Hispanic"),
pivot = "wider")## # A tibble: 2 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 0.511 0.0680
## 2 Hispanic 589 42 0.393 0.0280If you want to ignore more than one value per row or column, provide them in a named list:
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
ignore = list(race = c("Non-Black,Non-Hispanic", "Hispanic")),
pivot = "wider")## # A tibble: 1 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 0.882 0.118Use the margins argument to define how percentages are
calculated and shown in the resulting table:
# Default: percentages sum to one across the entire table
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
pivot = "wider")## # A tibble: 3 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 0.257 0.0343
## 2 Hispanic 589 42 0.198 0.0141
## 3 Non-Black,Non… 1394 83 0.468 0.0279
# Rowwise: percentages sum to one across rows
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
margins = "rows",
pivot = "wider")## # A tibble: 3 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 0.882 0.118
## 2 Hispanic 589 42 0.933 0.0666
## 3 Non-Black,Non… 1394 83 0.944 0.0562
# Columnwise: percentages sum to one across columns
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
margins = "columns",
pivot = "wider")## # A tibble: 3 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 0.279 0.449
## 2 Hispanic 589 42 0.214 0.185
## 3 Non-Black,Non… 1394 83 0.507 0.366Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE)## # A tibble: 6 × 4
## race bthwht count percent
## <chr> <dbl> <int> <dbl>
## 1 Black 0 766 0.257
## 2 Black 1 102 0.0343
## 3 Hispanic 0 589 0.198
## 4 Hispanic 1 42 0.0141
## 5 Non-Black,Non-Hispanic 0 1394 0.468
## 6 Non-Black,Non-Hispanic 1 83 0.0279
# Counts only
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
only = "count")## # A tibble: 6 × 3
## race bthwht count
## <chr> <dbl> <int>
## 1 Black 0 766
## 2 Black 1 102
## 3 Hispanic 0 589
## 4 Hispanic 1 42
## 5 Non-Black,Non-Hispanic 0 1394
## 6 Non-Black,Non-Hispanic 1 83
# Percents only
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
only = "percent")## # A tibble: 6 × 3
## race bthwht percent
## <chr> <dbl> <dbl>
## 1 Black 0 0.257
## 2 Black 1 0.0343
## 3 Hispanic 0 0.198
## 4 Hispanic 1 0.0141
## 5 Non-Black,Non-Hispanic 0 0.468
## 6 Non-Black,Non-Hispanic 1 0.0279Multiple response variables
Summarize multiple response variables
With select_tbl(), you can produce a summary table for
multiple response variables with the same variable stem. A variable stem
is a common prefix found in related variable names, often corresponding
to similar survey items, that represents a shared concept before unique
identifiers.
For example, the depressive dataset contains eight
variables that share the same variable stem: dep, with each
one representing a different item (such as a statement, question, or
indicator) used to measure depression:
names(depressive)## [1] "cid" "race" "sex" "yob" "dep_1" "dep_2" "dep_3" "dep_4" "dep_5"
## [10] "dep_6" "dep_7" "dep_8"With the select_tbl() function, you can summarize these
responses to see how many respondents selected each answer option for
every variable.
select_tbl(data = depressive, var_stem = "dep")## # A tibble: 24 × 4
## variable values count percent
## <chr> <int> <int> <dbl>
## 1 dep_1 1 109 0.0678
## 2 dep_1 2 689 0.429
## 3 dep_1 3 809 0.503
## 4 dep_2 1 144 0.0896
## 5 dep_2 2 746 0.464
## 6 dep_2 3 717 0.446
## 7 dep_3 1 1162 0.723
## 8 dep_3 2 392 0.244
## 9 dep_3 3 53 0.0330
## 10 dep_4 1 601 0.374
## # ℹ 14 more rowsYou can also use the ignore and na_removal
arguments to exclude values from the data and specify how missing values
should be handled. By default, missing values are removed
listwise, but you can set na_removal to
pairwise for pairwise removal instead:
# Default listwise removal, value '3' removed from data
select_tbl(data = depressive,
var_stem = "dep",
ignore = 3)## # A tibble: 16 × 4
## variable values count percent
## <chr> <int> <int> <dbl>
## 1 dep_1 1 37 0.167
## 2 dep_1 2 185 0.833
## 3 dep_2 1 47 0.212
## 4 dep_2 2 175 0.788
## 5 dep_3 1 133 0.599
## 6 dep_3 2 89 0.401
## 7 dep_4 1 108 0.486
## 8 dep_4 2 114 0.514
## 9 dep_5 1 61 0.275
## 10 dep_5 2 161 0.725
## 11 dep_6 1 86 0.387
## 12 dep_6 2 136 0.613
## 13 dep_7 1 126 0.568
## 14 dep_7 2 96 0.432
## 15 dep_8 1 81 0.365
## 16 dep_8 2 141 0.635
# Pairwise removal, value '3' removed from data
select_tbl(data = depressive,
var_stem = "dep",
ignore = 3,
na_removal = "pairwise")## # A tibble: 16 × 4
## variable values count percent
## <chr> <int> <int> <dbl>
## 1 dep_1 1 120 0.145
## 2 dep_1 2 709 0.855
## 3 dep_2 1 151 0.165
## 4 dep_2 2 762 0.835
## 5 dep_3 1 1192 0.746
## 6 dep_3 2 406 0.254
## 7 dep_4 1 611 0.416
## 8 dep_4 2 856 0.584
## 9 dep_5 1 206 0.264
## 10 dep_5 2 574 0.736
## 11 dep_6 1 399 0.312
## 12 dep_6 2 879 0.688
## 13 dep_7 1 1046 0.674
## 14 dep_7 2 507 0.326
## 15 dep_8 1 323 0.287
## 16 dep_8 2 801 0.713Set the pivot argument to wider to reshape
the resulting table into the wide format. By default, the summary table
is presented in the long format.
# Default longer format
select_tbl(data = depressive,
var_stem = "dep")## # A tibble: 24 × 4
## variable values count percent
## <chr> <int> <int> <dbl>
## 1 dep_1 1 109 0.0678
## 2 dep_1 2 689 0.429
## 3 dep_1 3 809 0.503
## 4 dep_2 1 144 0.0896
## 5 dep_2 2 746 0.464
## 6 dep_2 3 717 0.446
## 7 dep_3 1 1162 0.723
## 8 dep_3 2 392 0.244
## 9 dep_3 3 53 0.0330
## 10 dep_4 1 601 0.374
## # ℹ 14 more rows
# Wider format
select_tbl(data = depressive,
var_stem = "dep",
pivot = "wider")## # A tibble: 8 × 7
## variable count_value_1 count_value_2 count_value_3 percent_value_1
## <chr> <int> <int> <int> <dbl>
## 1 dep_1 109 689 809 0.0678
## 2 dep_2 144 746 717 0.0896
## 3 dep_3 1162 392 53 0.723
## 4 dep_4 601 836 170 0.374
## 5 dep_5 194 556 857 0.121
## 6 dep_6 387 860 360 0.241
## 7 dep_7 1028 490 89 0.640
## 8 dep_8 317 784 506 0.197
## # ℹ 2 more variables: percent_value_2 <dbl>, percent_value_3 <dbl>You can use the var_labels argument to include variable
labels in your summary table to make the variable names easier to
interpret:
select_tbl(data = depressive,
var_stem = "dep",
pivot = "wider",
var_labels = c(
dep_1="how often child feels sad and blue",
dep_2="how often child feels nervous, tense, or on edge",
dep_3="how often child feels happy",
dep_4="how often child feels bored",
dep_5="how often child feels lonely",
dep_6="how often child feels tired or worn out",
dep_7="how often child feels excited about something",
dep_8="how often child feels too busy to get everything"
)
)## # A tibble: 8 × 8
## variable variable_label count_value_1 count_value_2 count_value_3
## <chr> <chr> <int> <int> <int>
## 1 dep_1 how often child feels sad … 109 689 809
## 2 dep_2 how often child feels nerv… 144 746 717
## 3 dep_3 how often child feels happy 1162 392 53
## 4 dep_4 how often child feels bored 601 836 170
## 5 dep_5 how often child feels lone… 194 556 857
## 6 dep_6 how often child feels tire… 387 860 360
## 7 dep_7 how often child feels exci… 1028 490 89
## 8 dep_8 how often child feels too … 317 784 506
## # ℹ 3 more variables: percent_value_1 <dbl>, percent_value_2 <dbl>,
## # percent_value_3 <dbl>Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
select_tbl(data = depressive,
var_stem = "dep",
pivot = "wider")## # A tibble: 8 × 7
## variable count_value_1 count_value_2 count_value_3 percent_value_1
## <chr> <int> <int> <int> <dbl>
## 1 dep_1 109 689 809 0.0678
## 2 dep_2 144 746 717 0.0896
## 3 dep_3 1162 392 53 0.723
## 4 dep_4 601 836 170 0.374
## 5 dep_5 194 556 857 0.121
## 6 dep_6 387 860 360 0.241
## 7 dep_7 1028 490 89 0.640
## 8 dep_8 317 784 506 0.197
## # ℹ 2 more variables: percent_value_2 <dbl>, percent_value_3 <dbl>
# Counts only
select_tbl(data = depressive,
var_stem = "dep",
pivot = "wider",
only = "count")## # A tibble: 8 × 4
## variable count_value_1 count_value_2 count_value_3
## <chr> <int> <int> <int>
## 1 dep_1 109 689 809
## 2 dep_2 144 746 717
## 3 dep_3 1162 392 53
## 4 dep_4 601 836 170
## 5 dep_5 194 556 857
## 6 dep_6 387 860 360
## 7 dep_7 1028 490 89
## 8 dep_8 317 784 506
# Percents only
select_tbl(data = depressive,
var_stem = "dep",
pivot = "wider",
only = "percent")## # A tibble: 8 × 4
## variable percent_value_1 percent_value_2 percent_value_3
## <chr> <dbl> <dbl> <dbl>
## 1 dep_1 0.0678 0.429 0.503
## 2 dep_2 0.0896 0.464 0.446
## 3 dep_3 0.723 0.244 0.0330
## 4 dep_4 0.374 0.520 0.106
## 5 dep_5 0.121 0.346 0.533
## 6 dep_6 0.241 0.535 0.224
## 7 dep_7 0.640 0.305 0.0554
## 8 dep_8 0.197 0.488 0.315Summarize multiple response variables by group or pattern
With select_group_tbl(), you can create a summary table
for multiple response variables with the same variable stem, grouped
either by another variable in your dataset or by matching a pattern in
the variable names.
For example, we often want to summarize survey responses by demographic variables like gender, age, or race:
dep_recoded <-
depressive |>
dplyr::mutate(
race = dplyr::case_match(.x = race,
1 ~ "Hispanic",
2 ~ "Black",
3 ~ "Non-Black/Non-Hispanic",
.default = NA)
) |>
dplyr::mutate(
dplyr::across(
.cols = dplyr::starts_with("dep"),
.fns = ~ dplyr::case_when(.x == 1 ~ "often",
.x == 2 ~ "sometimes",
.x == 3 ~ "hardly ever")
)
) |>
dplyr::mutate(
dplyr::across(
.cols = dplyr::starts_with("dep"),
.fns = ~ factor(., levels = c("often", "sometimes", "hardly ever"),
labels = c("often", "sometimes", "hardly ever"))
)
)
# longer format
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer")## # A tibble: 72 × 5
## variable race values count percent
## <chr> <chr> <fct> <int> <dbl>
## 1 dep_1 Black often 45 0.0280
## 2 dep_1 Black sometimes 194 0.121
## 3 dep_1 Black hardly ever 248 0.154
## 4 dep_1 Hispanic often 28 0.0174
## 5 dep_1 Hispanic sometimes 155 0.0965
## 6 dep_1 Hispanic hardly ever 187 0.116
## 7 dep_1 Non-Black/Non-Hispanic often 36 0.0224
## 8 dep_1 Non-Black/Non-Hispanic sometimes 340 0.212
## 9 dep_1 Non-Black/Non-Hispanic hardly ever 374 0.233
## 10 dep_2 Black often 49 0.0305
## # ℹ 62 more rows
# wider format
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "wider")## # A tibble: 24 × 8
## variable values count_race_Black count_race_Hispanic count_race_Non-Black…¹
## <chr> <fct> <int> <int> <int>
## 1 dep_1 often 45 28 36
## 2 dep_1 sometim… 194 155 340
## 3 dep_1 hardly … 248 187 374
## 4 dep_2 often 49 36 59
## 5 dep_2 sometim… 204 163 379
## 6 dep_2 hardly … 234 171 312
## 7 dep_3 often 330 246 586
## 8 dep_3 sometim… 138 104 150
## 9 dep_3 hardly … 19 20 14
## 10 dep_4 often 183 131 287
## # ℹ 14 more rows
## # ℹ abbreviated name: ¹`count_race_Non-Black/Non-Hispanic`
## # ℹ 3 more variables: percent_race_Black <dbl>, percent_race_Hispanic <dbl>,
## # `percent_race_Non-Black/Non-Hispanic` <dbl>As with cat_group_tbl(), you can define values to
exclude and how to handle missing data. When excluding values, use the
var_stem argument to specify which values to omit across
variables sharing that stem.
# Default listwise removal: 'often' value removed from all
# dep_ variables, and 'Non-Black/Non-Hispanic' value removed
# from race variable
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
ignore = c(dep = "often", race = "Non-Black/Non-Hispanic"))## # A tibble: 31 × 5
## variable race values count percent
## <chr> <chr> <int> <int> <dbl>
## 1 dep_1 Black 2 16 0.254
## 2 dep_1 Black 3 18 0.286
## 3 dep_1 Hispanic 2 20 0.317
## 4 dep_1 Hispanic 3 9 0.143
## 5 dep_2 Black 2 16 0.254
## 6 dep_2 Black 3 18 0.286
## 7 dep_2 Hispanic 2 16 0.254
## 8 dep_2 Hispanic 3 13 0.206
## 9 dep_3 Black 2 34 0.540
## 10 dep_3 Hispanic 2 21 0.333
## # ℹ 21 more rows
# Pairwise removal: 'often' value removed from all
# dep_ variables, and 'Non-Black/Non-Hispanic' value removed
# from race variable
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
ignore = c(dep = "often", race = "Non-Black/Non-Hispanic"),
na_removal = "pairwise")## # A tibble: 32 × 5
## variable race values count percent
## <chr> <chr> <int> <int> <dbl>
## 1 dep_1 Black 2 203 0.251
## 2 dep_1 Black 3 256 0.317
## 3 dep_1 Hispanic 2 159 0.197
## 4 dep_1 Hispanic 3 190 0.235
## 5 dep_2 Black 2 213 0.269
## 6 dep_2 Black 3 241 0.305
## 7 dep_2 Hispanic 2 165 0.209
## 8 dep_2 Hispanic 3 172 0.217
## 9 dep_3 Black 2 149 0.507
## 10 dep_3 Black 3 20 0.0680
## # ℹ 22 more rowsUse a list if you want to exclude several values from the same
var_stem or group variable:
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
ignore = list(race = c("Hispanic", "Non-Black/Non-Hispanic")))## # A tibble: 24 × 5
## variable race values count percent
## <chr> <chr> <fct> <int> <dbl>
## 1 dep_1 Black often 45 0.0924
## 2 dep_1 Black sometimes 194 0.398
## 3 dep_1 Black hardly ever 248 0.509
## 4 dep_2 Black often 49 0.101
## 5 dep_2 Black sometimes 204 0.419
## 6 dep_2 Black hardly ever 234 0.480
## 7 dep_3 Black often 330 0.678
## 8 dep_3 Black sometimes 138 0.283
## 9 dep_3 Black hardly ever 19 0.0390
## 10 dep_4 Black often 183 0.376
## # ℹ 14 more rowsWhen group_type is variable you can use the
margins argument to define how percentages are calculated
and shown in the resulting table:
# Default: percentages for each variable sum to one.
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
only = "percent")## # A tibble: 72 × 4
## variable race values percent
## <chr> <chr> <fct> <dbl>
## 1 dep_1 Black often 0.0280
## 2 dep_1 Black sometimes 0.121
## 3 dep_1 Black hardly ever 0.154
## 4 dep_1 Hispanic often 0.0174
## 5 dep_1 Hispanic sometimes 0.0965
## 6 dep_1 Hispanic hardly ever 0.116
## 7 dep_1 Non-Black/Non-Hispanic often 0.0224
## 8 dep_1 Non-Black/Non-Hispanic sometimes 0.212
## 9 dep_1 Non-Black/Non-Hispanic hardly ever 0.233
## 10 dep_2 Black often 0.0305
## # ℹ 62 more rows
# Rowwise: for each value of the variable, the percentages
# for all levels of the grouping variable sum to one
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
margins = "rows",
pivot = "longer",
only = "percent")## # A tibble: 72 × 4
## variable race values percent
## <chr> <chr> <fct> <dbl>
## 1 dep_1 Black often 0.413
## 2 dep_1 Hispanic often 0.257
## 3 dep_1 Non-Black/Non-Hispanic often 0.330
## 4 dep_1 Black sometimes 0.282
## 5 dep_1 Hispanic sometimes 0.225
## 6 dep_1 Non-Black/Non-Hispanic sometimes 0.493
## 7 dep_1 Black hardly ever 0.307
## 8 dep_1 Hispanic hardly ever 0.231
## 9 dep_1 Non-Black/Non-Hispanic hardly ever 0.462
## 10 dep_2 Black often 0.340
## # ℹ 62 more rows
# Columnwise: for each level of the grouping variable,
# the percentages for all values of the main variable
# sum to one
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
margins = "columns",
pivot = "longer",
only = "percent")## # A tibble: 72 × 4
## variable race values percent
## <chr> <chr> <fct> <dbl>
## 1 dep_1 Black often 0.0924
## 2 dep_1 Black sometimes 0.398
## 3 dep_1 Black hardly ever 0.509
## 4 dep_1 Hispanic often 0.0757
## 5 dep_1 Hispanic sometimes 0.419
## 6 dep_1 Hispanic hardly ever 0.505
## 7 dep_1 Non-Black/Non-Hispanic often 0.048
## 8 dep_1 Non-Black/Non-Hispanic sometimes 0.453
## 9 dep_1 Non-Black/Non-Hispanic hardly ever 0.499
## 10 dep_2 Black often 0.101
## # ℹ 62 more rowsAnother application of select_group_tbl is summarizing
responses based on a matching pattern, such as survey time points (e.g.,
waves). To use this feature, set group_type to
pattern and enter the pattern to search for in the
group argument.
For example, the stem_social_psych dataset includes a
set of variables responded to by students at two different time points
(“w1” and “w2”). You can summarize the responses for these variables
using the following approach:
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
pivot = "longer")## # A tibble: 10 × 5
## variable group values count percent
## <chr> <chr> <dbl> <int> <dbl>
## 1 belong_belongStem_w1 w1 1 5 0.0185
## 2 belong_belongStem_w1 w1 2 20 0.0741
## 3 belong_belongStem_w1 w1 3 59 0.219
## 4 belong_belongStem_w1 w1 4 107 0.396
## 5 belong_belongStem_w1 w1 5 79 0.293
## 6 belong_belongStem_w2 w2 1 11 0.0407
## 7 belong_belongStem_w2 w2 2 11 0.0407
## 8 belong_belongStem_w2 w2 3 44 0.163
## 9 belong_belongStem_w2 w2 4 113 0.419
## 10 belong_belongStem_w2 w2 5 91 0.337Use the group_name argument to assign a descriptive name
to the column containing the matched pattern values.
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer")## # A tibble: 10 × 5
## variable wave values count percent
## <chr> <chr> <dbl> <int> <dbl>
## 1 belong_belongStem_w1 w1 1 5 0.0185
## 2 belong_belongStem_w1 w1 2 20 0.0741
## 3 belong_belongStem_w1 w1 3 59 0.219
## 4 belong_belongStem_w1 w1 4 107 0.396
## 5 belong_belongStem_w1 w1 5 79 0.293
## 6 belong_belongStem_w2 w2 1 11 0.0407
## 7 belong_belongStem_w2 w2 2 11 0.0407
## 8 belong_belongStem_w2 w2 3 44 0.163
## 9 belong_belongStem_w2 w2 4 113 0.419
## 10 belong_belongStem_w2 w2 5 91 0.337You can use the var_labels argument to include variable
labels in your summary table to make the variable names easier to
interpret:
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
var_labels = c(
belong_belongStem_w1 = "I feel like I belong in STEM (wave 1)",
belong_belongStem_w2 = "I feel like I belong in STEM (wave 2)"
))## # A tibble: 10 × 6
## variable variable_label wave values count percent
## <chr> <chr> <chr> <dbl> <int> <dbl>
## 1 belong_belongStem_w1 I feel like I belong in STEM… w1 1 5 0.0185
## 2 belong_belongStem_w1 I feel like I belong in STEM… w1 2 20 0.0741
## 3 belong_belongStem_w1 I feel like I belong in STEM… w1 3 59 0.219
## 4 belong_belongStem_w1 I feel like I belong in STEM… w1 4 107 0.396
## 5 belong_belongStem_w1 I feel like I belong in STEM… w1 5 79 0.293
## 6 belong_belongStem_w2 I feel like I belong in STEM… w2 1 11 0.0407
## 7 belong_belongStem_w2 I feel like I belong in STEM… w2 2 11 0.0407
## 8 belong_belongStem_w2 I feel like I belong in STEM… w2 3 44 0.163
## 9 belong_belongStem_w2 I feel like I belong in STEM… w2 4 113 0.419
## 10 belong_belongStem_w2 I feel like I belong in STEM… w2 5 91 0.337Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
only = "count")## # A tibble: 10 × 4
## variable wave values count
## <chr> <chr> <dbl> <int>
## 1 belong_belongStem_w1 w1 1 5
## 2 belong_belongStem_w1 w1 2 20
## 3 belong_belongStem_w1 w1 3 59
## 4 belong_belongStem_w1 w1 4 107
## 5 belong_belongStem_w1 w1 5 79
## 6 belong_belongStem_w2 w2 1 11
## 7 belong_belongStem_w2 w2 2 11
## 8 belong_belongStem_w2 w2 3 44
## 9 belong_belongStem_w2 w2 4 113
## 10 belong_belongStem_w2 w2 5 91
# Counts only
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
only = "count")## # A tibble: 10 × 4
## variable wave values count
## <chr> <chr> <dbl> <int>
## 1 belong_belongStem_w1 w1 1 5
## 2 belong_belongStem_w1 w1 2 20
## 3 belong_belongStem_w1 w1 3 59
## 4 belong_belongStem_w1 w1 4 107
## 5 belong_belongStem_w1 w1 5 79
## 6 belong_belongStem_w2 w2 1 11
## 7 belong_belongStem_w2 w2 2 11
## 8 belong_belongStem_w2 w2 3 44
## 9 belong_belongStem_w2 w2 4 113
## 10 belong_belongStem_w2 w2 5 91
# Percents only
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
only = "percent")## # A tibble: 10 × 4
## variable wave values percent
## <chr> <chr> <dbl> <dbl>
## 1 belong_belongStem_w1 w1 1 0.0185
## 2 belong_belongStem_w1 w1 2 0.0741
## 3 belong_belongStem_w1 w1 3 0.219
## 4 belong_belongStem_w1 w1 4 0.396
## 5 belong_belongStem_w1 w1 5 0.293
## 6 belong_belongStem_w2 w2 1 0.0407
## 7 belong_belongStem_w2 w2 2 0.0407
## 8 belong_belongStem_w2 w2 3 0.163
## 9 belong_belongStem_w2 w2 4 0.419
## 10 belong_belongStem_w2 w2 5 0.337Continuous variables
Summarize continuous variables
With the mean_tbl() function, you can summarize a series
of continuous variables that share the same variable stem. The resulting
table provides descriptive statistics for each variable, including the
mean, standard deviation, minimum, maximum, and the number of
non-missing values.
mean_tbl(data = sdoh,
var_stem = "HHC_PCT")## # A tibble: 6 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
## 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8 0 100 3227
## 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3 0 100 3227
## 4 HHC_PCT_HHA_SPEECH 49.1 47.6 0 100 3227
## 5 HHC_PCT_HHA_MEDICAL 42.2 46.2 0 100 3227
## 6 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227Like the other functions in this package, you can use the
ignore argument to specify which values to exclude from all
variables associated with the provided variable stem.
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
ignore = 0)## # A tibble: 6 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING 100 0 100 100 1454
## 2 HHC_PCT_HHA_PHYS_THERAPY 98.0 7.52 25 100 1454
## 3 HHC_PCT_HHA_OCC_THERAPY 94.9 12.7 25 100 1454
## 4 HHC_PCT_HHA_SPEECH 91.9 16.6 20 100 1454
## 5 HHC_PCT_HHA_MEDICAL 87.7 20.2 9.09 100 1454
## 6 HHC_PCT_HHA_AIDE 96.6 9.75 42.9 100 1454You can also specify how missing values are removed:
# Default listwise removal
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
ignore = 0)## # A tibble: 6 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING 100 0 100 100 1454
## 2 HHC_PCT_HHA_PHYS_THERAPY 98.0 7.52 25 100 1454
## 3 HHC_PCT_HHA_OCC_THERAPY 94.9 12.7 25 100 1454
## 4 HHC_PCT_HHA_SPEECH 91.9 16.6 20 100 1454
## 5 HHC_PCT_HHA_MEDICAL 87.7 20.2 9.09 100 1454
## 6 HHC_PCT_HHA_AIDE 96.6 9.75 42.9 100 1454
# Pairwise removal
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
na_removal = "pairwise",
ignore = 0)## # A tibble: 6 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING 100 0 100 100 1879
## 2 HHC_PCT_HHA_PHYS_THERAPY 98.1 7.84 25 100 1865
## 3 HHC_PCT_HHA_OCC_THERAPY 95.2 12.8 25 100 1776
## 4 HHC_PCT_HHA_SPEECH 92.4 16.4 20 100 1714
## 5 HHC_PCT_HHA_MEDICAL 88.1 20.1 9.09 100 1547
## 6 HHC_PCT_HHA_AIDE 97.1 9.26 33.3 100 1833Including variable labels in your summary table can help make the variable names easier to interpret.
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
na_removal = "pairwise",
var_labels = c(
HHC_PCT_HHA_NURSING="% of agencies offering nursing care services",
HHC_PCT_HHA_PHYS_THERAPY="% of agencies offering physical therapy services",
HHC_PCT_HHA_OCC_THERAPY="% of agencies offering occupational therapy services",
HHC_PCT_HHA_SPEECH="% of agencies offering speech pathology services",
HHC_PCT_HHA_MEDICAL="% of agencies offering medical social services",
HHC_PCT_HHA_AIDE="% of agencies offering home health aide services"
))## # A tibble: 6 × 7
## variable variable_label mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING % of agencies offering… 58.2 49.3 0 100 3227
## 2 HHC_PCT_HHA_PHYS_THERAPY % of agencies offering… 56.7 48.8 0 100 3227
## 3 HHC_PCT_HHA_OCC_THERAPY % of agencies offering… 52.4 48.3 0 100 3227
## 4 HHC_PCT_HHA_SPEECH % of agencies offering… 49.1 47.6 0 100 3227
## 5 HHC_PCT_HHA_MEDICAL % of agencies offering… 42.2 46.2 0 100 3227
## 6 HHC_PCT_HHA_AIDE % of agencies offering… 55.1 48.6 0 100 3227Finally, you can choose what information to return using the
only argument.
# Default: all summary statistics returned
# (mean, sd, min, max, nobs)
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
na_removal = "pairwise")## # A tibble: 6 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
## 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8 0 100 3227
## 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3 0 100 3227
## 4 HHC_PCT_HHA_SPEECH 49.1 47.6 0 100 3227
## 5 HHC_PCT_HHA_MEDICAL 42.2 46.2 0 100 3227
## 6 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
# Means and non-missing observations returned
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
na_removal = "pairwise",
only = c("mean", "nobs"))## # A tibble: 6 × 3
## variable mean nobs
## <chr> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING 58.2 3227
## 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 3227
## 3 HHC_PCT_HHA_OCC_THERAPY 52.4 3227
## 4 HHC_PCT_HHA_SPEECH 49.1 3227
## 5 HHC_PCT_HHA_MEDICAL 42.2 3227
## 6 HHC_PCT_HHA_AIDE 55.1 3227
# Means and standard deviations returned
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
na_removal = "pairwise",
only = c("mean", "sd"))## # A tibble: 6 × 3
## variable mean sd
## <chr> <dbl> <dbl>
## 1 HHC_PCT_HHA_NURSING 58.2 49.3
## 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8
## 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3
## 4 HHC_PCT_HHA_SPEECH 49.1 47.6
## 5 HHC_PCT_HHA_MEDICAL 42.2 46.2
## 6 HHC_PCT_HHA_AIDE 55.1 48.6Summarize continuous variables by group or pattern
With the mean_group_tbl() function, you can produce a
summary table for a series of continuous variables sharing the same
variable stem, grouped either by another variable in your dataset or by
matching a pattern in the variable names.
For example, we often want to present summary statistics for responses by demographic variables like region, gender, age, or race:
mean_group_tbl(data = sdoh,
var_stem = "HHC_PCT",
group = "REGION",
group_type = "variable")## # A tibble: 24 × 7
## variable REGION mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING Midwest 57.4 49.5 0 100 1055
## 2 HHC_PCT_HHA_NURSING Northeast 74.2 43.9 0 100 217
## 3 HHC_PCT_HHA_NURSING South 58.8 49.2 0 100 1422
## 4 HHC_PCT_HHA_NURSING West 56 49.7 0 100 450
## 5 HHC_PCT_HHA_PHYS_THERAPY Midwest 55.2 48.9 0 100 1055
## 6 HHC_PCT_HHA_PHYS_THERAPY Northeast 68.0 43.1 0 100 217
## 7 HHC_PCT_HHA_PHYS_THERAPY South 58.4 49.0 0 100 1422
## 8 HHC_PCT_HHA_PHYS_THERAPY West 54.5 49.0 0 100 450
## 9 HHC_PCT_HHA_OCC_THERAPY Midwest 52.9 48.7 0 100 1055
## 10 HHC_PCT_HHA_OCC_THERAPY Northeast 64.8 42.8 0 100 217
## # ℹ 14 more rowsAs with select_group_tbl(), you can specify which values
to exclude and whether to omit missing values. Be sure to use the
var_stem argument you provide to determine which values to
exclude for variables sharing the same stem.
# Default listwise removal
mean_group_tbl(data = sdoh,
var_stem = "HHC_PCT",
group = "REGION",
ignore = c(HHC_PCT = 0, REGION = "Northeast"))## # A tibble: 18 × 7
## variable REGION mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING Midwest 100 0 100 100 403
## 2 HHC_PCT_HHA_NURSING South 100 0 100 100 681
## 3 HHC_PCT_HHA_NURSING West 100 0 100 100 200
## 4 HHC_PCT_HHA_PHYS_THERAPY Midwest 97.7 7.15 50 100 403
## 5 HHC_PCT_HHA_PHYS_THERAPY South 99.2 4.78 50 100 681
## 6 HHC_PCT_HHA_PHYS_THERAPY West 98.3 5.31 60 100 200
## 7 HHC_PCT_HHA_OCC_THERAPY Midwest 96.3 10.4 33.3 100 403
## 8 HHC_PCT_HHA_OCC_THERAPY South 95.5 12.4 28.6 100 681
## 9 HHC_PCT_HHA_OCC_THERAPY West 94.8 12.2 25 100 200
## 10 HHC_PCT_HHA_SPEECH Midwest 91.9 16.2 33.3 100 403
## 11 HHC_PCT_HHA_SPEECH South 93.4 15.3 25 100 681
## 12 HHC_PCT_HHA_SPEECH West 91.0 17.2 20 100 200
## 13 HHC_PCT_HHA_MEDICAL Midwest 82.4 23.8 9.09 100 403
## 14 HHC_PCT_HHA_MEDICAL South 89.4 18.6 16.7 100 681
## 15 HHC_PCT_HHA_MEDICAL West 92.6 15.3 33.3 100 200
## 16 HHC_PCT_HHA_AIDE Midwest 97.3 8.97 50 100 403
## 17 HHC_PCT_HHA_AIDE South 96.1 10.3 42.9 100 681
## 18 HHC_PCT_HHA_AIDE West 96.4 9.96 50 100 200
# Pairwise removal
mean_group_tbl(data = sdoh,
var_stem = "HHC_PCT",
group = "REGION",
na_removal = "pairwise",
ignore = c(HHC_PCT = 0, REGION = "Northeast"))## # A tibble: 18 × 7
## variable REGION mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING Midwest 100 0 100 100 606
## 2 HHC_PCT_HHA_NURSING South 100 0 100 100 836
## 3 HHC_PCT_HHA_NURSING West 100 0 100 100 252
## 4 HHC_PCT_HHA_PHYS_THERAPY Midwest 97.8 8.36 25 100 595
## 5 HHC_PCT_HHA_PHYS_THERAPY South 99.4 4.32 50 100 836
## 6 HHC_PCT_HHA_PHYS_THERAPY West 97.7 8.14 33.3 100 251
## 7 HHC_PCT_HHA_OCC_THERAPY Midwest 96.3 11.5 25 100 579
## 8 HHC_PCT_HHA_OCC_THERAPY South 95.8 12.2 28.6 100 787
## 9 HHC_PCT_HHA_OCC_THERAPY West 94.5 13.0 25 100 232
## 10 HHC_PCT_HHA_SPEECH Midwest 92.6 16.1 25 100 552
## 11 HHC_PCT_HHA_SPEECH South 93.7 15.2 25 100 769
## 12 HHC_PCT_HHA_SPEECH West 91.3 17.0 20 100 221
## 13 HHC_PCT_HHA_MEDICAL Midwest 83.0 23.6 9.09 100 419
## 14 HHC_PCT_HHA_MEDICAL South 89.7 18.6 16.7 100 724
## 15 HHC_PCT_HHA_MEDICAL West 92.5 15.8 33.3 100 224
## 16 HHC_PCT_HHA_AIDE Midwest 98.0 7.85 50 100 588
## 17 HHC_PCT_HHA_AIDE South 96.6 9.82 42.9 100 816
## 18 HHC_PCT_HHA_AIDE West 96.4 10.8 33.3 100 247Use a list if you want to exclude several values from the same
var_stem or group variable:
mean_group_tbl(data = sdoh,
var_stem = "HHC_PCT",
group = "REGION",
na_removal = "pairwise",
ignore = list(HHC_PCT = 0, REGION = c("Northeast", "South")
))## # A tibble: 12 × 7
## variable REGION mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HHC_PCT_HHA_NURSING Midwest 100 0 100 100 606
## 2 HHC_PCT_HHA_NURSING West 100 0 100 100 252
## 3 HHC_PCT_HHA_PHYS_THERAPY Midwest 97.8 8.36 25 100 595
## 4 HHC_PCT_HHA_PHYS_THERAPY West 97.7 8.14 33.3 100 251
## 5 HHC_PCT_HHA_OCC_THERAPY Midwest 96.3 11.5 25 100 579
## 6 HHC_PCT_HHA_OCC_THERAPY West 94.5 13.0 25 100 232
## 7 HHC_PCT_HHA_SPEECH Midwest 92.6 16.1 25 100 552
## 8 HHC_PCT_HHA_SPEECH West 91.3 17.0 20 100 221
## 9 HHC_PCT_HHA_MEDICAL Midwest 83.0 23.6 9.09 100 419
## 10 HHC_PCT_HHA_MEDICAL West 92.5 15.8 33.3 100 224
## 11 HHC_PCT_HHA_AIDE Midwest 98.0 7.85 50 100 588
## 12 HHC_PCT_HHA_AIDE West 96.4 10.8 33.3 100 247Another application of mean_group_tbl is summarizing
responses based on a matching pattern, such as survey time points (e.g.,
waves). To use this feature, set group_type to
pattern and enter the pattern to search for in the
group argument.
set.seed(0803)
symptoms_data <-
data.frame(
symptoms_t1 = sample(c(0:10, -999), replace = TRUE, size = 50),
symptoms_t2 = sample(c(NA, 0:10, -999), replace = TRUE, size = 50),
symptoms_t3 = sample(c(NA, 0:10, -999), replace = TRUE, size = 50)
)
mean_group_tbl(data = symptoms_data,
var_stem = "symptoms",
group = "_t\\d",
group_type = "pattern",
ignore = c(symptoms = -999))## # A tibble: 3 × 7
## variable group mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 symptoms_t1 t1 4.03 3.14 0 10 33
## 2 symptoms_t2 t2 5.12 3.33 0 10 33
## 3 symptoms_t3 t3 4.64 3.29 0 10 33Use the group_name argument to give a descriptive label
to the column with matched patterns or grouping variable values, and the
var_labels argument to add labels to the variables in the
summary table.
mean_group_tbl(data = symptoms_data,
var_stem = "symptoms",
group = "_t\\d",
group_type = "pattern",
group_name = "time_point",
ignore = c(symptoms = -999),
var_labels = c(
symptoms_t1 = "# of symptoms at baseline",
symptoms_t2 = "# of symptoms at 6 months follow up",
symptoms_t3 = "# of symptoms at one-year follow up"))## # A tibble: 3 × 8
## variable variable_label time_point mean sd min max nobs
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 symptoms_t1 # of symptoms at baseline t1 4.03 3.14 0 10 33
## 2 symptoms_t2 # of symptoms at 6 month… t2 5.12 3.33 0 10 33
## 3 symptoms_t3 # of symptoms at one-yea… t3 4.64 3.29 0 10 33Finally, you can choose what information to return using the
only argument.
# Default: all summary statistics returned
# (mean, sd, min, max, nobs)
mean_group_tbl(data = symptoms_data,
var_stem = "symptoms",
group = "_t\\d",
group_type = "pattern",
group_name = "time_point",
ignore = c(symptoms = -999))## # A tibble: 3 × 7
## variable time_point mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 symptoms_t1 t1 4.03 3.14 0 10 33
## 2 symptoms_t2 t2 5.12 3.33 0 10 33
## 3 symptoms_t3 t3 4.64 3.29 0 10 33
# Means and non-missing observations only
mean_group_tbl(data = symptoms_data,
var_stem = "symptoms",
group = "_t\\d",
group_type = "pattern",
group_name = "time_point",
ignore = c(symptoms = -999),
only = c("mean", "nobs"))## # A tibble: 3 × 4
## variable time_point mean nobs
## <chr> <chr> <dbl> <int>
## 1 symptoms_t1 t1 4.03 33
## 2 symptoms_t2 t2 5.12 33
## 3 symptoms_t3 t3 4.64 33
# Means and standard deviations only
mean_group_tbl(data = symptoms_data,
var_stem = "symptoms",
group = "_t\\d",
group_type = "pattern",
group_name = "time_point",
ignore = c(symptoms = -999),
only = c("mean", "sd"))## # A tibble: 3 × 4
## variable time_point mean sd
## <chr> <chr> <dbl> <dbl>
## 1 symptoms_t1 t1 4.03 3.14
## 2 symptoms_t2 t2 5.12 3.33
## 3 symptoms_t3 t3 4.64 3.29